Search Results for author:
Found 18 papers, 10 papers with code
Safe reinforcement learning in uncertain contexts
In this work, we drop this assumption and show how we can perform safe learning when we cannot directly measure the context variables.
Non-ergodicity in reinforcement learning: robustness via ergodicity transformations
The expected value is the average over the statistical ensemble of infinitely many trajectories.
A computationally lightweight safe learning algorithm
Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems.
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
Model-free learning of communication and control policies provides an alternative.
On the trade-off between event-based and periodic state estimation under bandwidth constraints
In this article, we discuss, for a specific example, when the additional complexity of event-based methods is beneficial.
Towards remote fault detection by analyzing communication priorities
We propose a fault detection method that uses these priorities to detect errors in other agents.
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i. i. d.
Model-based Reinforcement Learning
reinforcement-learning
+1
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage.
GoSafe: Globally Optimal Safe Robot Learning
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage.
Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
An important class of cyber-physical systems relies on multiple agents that jointly perform a task by coordinating their actions over a wireless network.
Robot Learning with Crash Constraints
We consider failing behaviors as those that violate a constraint and address the problem of learning with crash constraints, where no data is obtained upon constraint violation.
Learning Event-triggered Control from Data through Joint Optimization
We present a framework for model-free learning of event-triggered control strategies.
Identifying Causal Structure in Dynamical Systems
In this paper, we propose a method that identifies the causal structure of control systems.
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
When learning to ride a bike, a child falls down a number of times before achieving the first success.
A Kernel Two-sample Test for Dynamical Systems
Evaluating whether data streams are drawn from the same distribution is at the heart of various machine learning problems.
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging.
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control with a significantly lower number of samples compared to usual, time-triggered methods.
Event-triggered Learning for Resource-efficient Networked Control
Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly.
